Active and resting motor threshold are efficiently obtained with adaptive threshold hunting.
Active and resting motor threshold are efficiently obtained with adaptive threshold hunting.
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DOI：
10.1371/journal.pone.0186007
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Nelson AJ
中科院分区：
文献类型：
--
作者：
Ah Sen CB;Fassett HJ;El-Sayes J;Turco CV;Hameer MM;Nelson AJ
经颅磁学研究通常依赖于活动和静息运动阈值的测量(即AMT、RMT)。前人的工作已经证明，自适应阈值搜索方法对于估计RMT是有效的。到目前为止，还没有研究比较AMT测量的运动阈值估计方法，但这一测量在探测皮质内回路的经颅磁刺激(TMS)研究中是基本的。本研究比较了两种获取AMT和RMT的方法：Rossini-Rothwell(R-R)相对频率估计法和基于序贯检验最大似然参数估计的自适应门限搜索方法(ML-PEST)。在实验者盲的受试者内研究设计中，通过R-R和ML-PEST方法对15名健康的右利手参与者的AMT和RMT进行了量化。用R-R和ML-PEST方法得到的AMT和RMT估计没有什么不同，具有很强的组内相关性和良好的一致性限度。然而，对于AMT和RMT估计，ML-PEST分别比R-R方法少17个和15个刺激。ML-PEST在减少估计AMT和RMT所需的TMS脉冲数量方面是有效的，而不会影响这些估计的准确性。使用ML-PEST来估计AMT和RMT提高了TMS实验的效率，因为它减少了获得这些测量的脉冲数量，而不会影响精度。当在一个会话中测试多个目标肌肉时，使用ML-PEST方法的好处被放大。
Transcranial magnetic studies typically rely on measures of active and resting motor threshold (i.e. AMT, RMT). Previous work has demonstrated that adaptive threshold hunting approaches are efficient for estimating RMT. To date, no study has compared motor threshold estimation approaches for measures of AMT, yet this measure is fundamental in transcranial magnetic stimulation (TMS) studies that probe intracortical circuits. The present study compared two methods for acquiring AMT and RMT: the Rossini-Rothwell (R-R) relative-frequency estimation method and an adaptive threshold-hunting method based on maximum-likelihood parameter estimation by sequential testing (ML-PEST). AMT and RMT were quantified via the R-R and ML-PEST methods in 15 healthy right-handed participants in an experimenter-blinded within-subject study design. AMT and RMT estimations obtained with both the R-R and ML-PEST approaches were not different, with strong intraclass correlation and good limits of agreement. However, ML-PEST required 17 and 15 fewer stimuli than the R-R method for the AMT and RMT estimation, respectively. ML-PEST is effective in reducing the number of TMS pulses required to estimate AMT and RMT without compromising the accuracy of these estimates. Using ML-PEST to estimate AMT and RMT increases the efficiency of the TMS experiment as it reduces the number of pulses to acquire these measures without compromising accuracy. The benefits of using the ML-PEST approach are amplified when multiple target muscles are tested within a session.